{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier with Randomized Search\n",
    "Decision tree is trained on feature set 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 32) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 32) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_32train_std.shape, \"and labels: \", y_32_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_32test_std), X_32test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_32test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train decision tree classifier - randomized search for parameter optimization\n",
      "Randomized search took 0.00 minutes \n",
      "   \n",
      "Result of randomized search:\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=13,\n",
      "            max_features=None, max_leaf_nodes=97, min_impurity_split=1e-07,\n",
      "            min_samples_leaf=1, min_samples_split=4,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
      "            splitter='best')\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "params = {'max_depth': list(range(9,18)),'max_leaf_nodes': list(range(50, 150)), 'min_samples_split': [2,3,4],\n",
    "          'max_features': [None,'auto','sqrt','log2'], 'criterion':['gini', 'entropy']}\n",
    "\n",
    "rand_search_cv = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params, verbose=0)\n",
    "\n",
    "start = time()\n",
    "print(\"Train decision tree classifier - randomized search for parameter optimization\")\n",
    "rand_search_cv.fit(X_32train_std, y_32_train)\n",
    "print(\"Randomized search took %.2f minutes \"%((time() - start)//60))\n",
    "print(\"   \")\n",
    "print(\"Result of randomized search:\")\n",
    "print(rand_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "Decision Tree's accuracy on -20 dB SNR samples =  0.1205\n",
      "Decision Tree's accuracy on -18 dB SNR samples =  0.125\n",
      "Decision Tree's accuracy on -16 dB SNR samples =  0.13125\n",
      "Decision Tree's accuracy on -14 dB SNR samples =  0.13175\n",
      "Decision Tree's accuracy on -12 dB SNR samples =  0.149\n",
      "Decision Tree's accuracy on -10 dB SNR samples =  0.1815\n",
      "Decision Tree's accuracy on -8 dB SNR samples =  0.2745\n",
      "Decision Tree's accuracy on -6 dB SNR samples =  0.358\n",
      "Decision Tree's accuracy on -4 dB SNR samples =  0.39575\n",
      "Decision Tree's accuracy on -2 dB SNR samples =  0.42425\n",
      "Decision Tree's accuracy on 0 dB SNR samples =  0.4995\n",
      "Decision Tree's accuracy on 2 dB SNR samples =  0.6085\n",
      "Decision Tree's accuracy on 4 dB SNR samples =  0.7755\n",
      "Decision Tree's accuracy on 6 dB SNR samples =  0.81775\n",
      "Decision Tree's accuracy on 8 dB SNR samples =  0.823\n",
      "Decision Tree's accuracy on 10 dB SNR samples =  0.82725\n",
      "Decision Tree's accuracy on 12 dB SNR samples =  0.83075\n",
      "Decision Tree's accuracy on 14 dB SNR samples =  0.83475\n",
      "Decision Tree's accuracy on 16 dB SNR samples =  0.82925\n",
      "Decision Tree's accuracy on 18 dB SNR samples =  0.8265\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = rand_search_cv.predict(X_32test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_32_test[snr], y_pred[snr])\n",
    "    print(\"Decision Tree's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dLviqaiWEJ62QIJPJcPv2bZw7dw5nz55VK3AtLS2rvSuZqsdHcX18fKq8oKl9+/Zo2bKl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ac9c93dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    448     4      0     0     3     46     44    11\n",
      "BPSK      1   477      0     0    13      3      6     3\n",
      "CPFSK     5     0    497     2     0     16      9     0\n",
      "GFSK     16     1      3   498     1      8     13     4\n",
      "PAM4      2    18      0     0   483      7      9     3\n",
      "QAM16     1     0      0     0     0    162    145     6\n",
      "QAM64     7     0      0     0     0    238    270     2\n",
      "QPSK     20     0      0     0     0     20      4   471\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = rand_search_cv.predict(X_32test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_32_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.81  0.01   0.00  0.00  0.01   0.08   0.08  0.02\n",
      "BPSK   0.00  0.95   0.00  0.00  0.03   0.01   0.01  0.01\n",
      "CPFSK  0.01  0.00   0.94  0.00  0.00   0.03   0.02  0.00\n",
      "GFSK   0.03  0.00   0.01  0.92  0.00   0.01   0.02  0.01\n",
      "PAM4   0.00  0.03   0.00  0.00  0.93   0.01   0.02  0.01\n",
      "QAM16  0.00  0.00   0.00  0.00  0.00   0.52   0.46  0.02\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.46   0.52  0.00\n",
      "QPSK   0.04  0.00   0.00  0.00  0.00   0.04   0.01  0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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n6Z8hPdg0M7M+prCiTf5Xs2tcXtXdpxFxYzYaaN2IeKHo0P8CXbJEgZmZtbau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\nAO/keyCFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc101f5c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_tree3.1.pkl']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(rand_search_cv, \"decision_tree3.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "rand_search_cv = joblib.load(\"decision_tree3.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
